Substrate Specificity Of Protease Research Articles

Elucidating the Substrate Envelope of Enterovirus 68-3C Protease: Structural Basis of Specificity and Potential Resistance.

Enterovirus-D68 (EV68) has emerged as a global health concern over the last decade with severe symptomatic infections resulting in long-lasting neurological deficits and death. Unfortunately, there are currently no FDA-approved antiviral drugs for EV68 or any other non-polio enterovirus. One particularly attractive class of potential drugs are small molecules inhibitors, which can target the conserved active site of EV68-3C protease. For other viral proteases, we have demonstrated that the emergence of drug resistance can be minimized by designing inhibitors that leverage the evolutionary constraints of substrate specificity. However, the structural characterization of EV68-3C protease bound to its substrates has been lacking. Here, we have determined the substrate specificity of EV68-3C protease through molecular modeling, molecular dynamics (MD) simulations, and co-crystal structures. Molecular models enabled us to successfully characterize the conserved hydrogen-bond networks between EV68-3C protease and the peptides corresponding to the viral cleavage sites. In addition, co-crystal structures we determined have revealed substrate-induced conformational changes of the protease which involved new interactions, primarily surrounding the S1 pocket. We calculated the substrate envelope, the three-dimensional consensus volume occupied by the substrates within the active site. With the elucidation of the EV68-3C protease substrate envelope, we evaluated how 3C protease inhibitors, AG7088 and SG-85, fit within the active site to predict potential resistance mutations.

Use of protease substrate specificity screening in the rational design of selective protease inhibitors with unnatural amino acids: Application to HGFA, matriptase, and hepsin.

Inhibition of the proteolytic processing of hepatocyte growth factor (HGF) and macrophage stimulating protein (MSP) is an attractive approach for the drug discovery of novel anticancer therapeutics which prevent tumor progression and metastasis. Here, we utilized an improved and expanded version of positional scanning of substrate combinatorial libraries (PS-SCL) technique called HyCoSuL to optimize peptidomimetic inhibitors of the HGF/MSP activating serine proteases, HGFA, matriptase, and hepsin. These inhibitors have an electrophilic ketone serine trapping warhead and thus form a reversible covalent bond to the protease. We demonstrate that by varying the P2, P3, and P4 positions of the inhibitor with unnatural amino acids based on the protease substrate preferences learned from HyCoSuL, we can predictably modify the potency and selectivity of the inhibitor. We identified the tetrapeptide JH-1144 (8) as a single digit nM inhibitor of HGFA, matriptase and hepsin with excellent selectivity over Factor Xa and thrombin. These unnatural peptides have increased metabolic stability relative to natural peptides of similar structure. The tripeptide inhibitor PK-1-89 (2) has excellent pharmacokinetics in mice with good compound exposure out to 24 h. In addition, we obtained an X-ray structure of the inhibitor MM1132 (15) bound to matriptase revealing an interesting binding conformation useful for future inhibitor design.

High-Resolution Substrate Specificity Profiling of SARS-CoV-2 Mpro; Comparison to SARS-CoV Mpro.

The SARS-CoV-2 Mpro protease from COVID-19 cleaves the pp1a and pp2b polyproteins at 11 sites during viral maturation and is the target of Nirmatrelvir, one of the two components of the frontline treatment sold as Paxlovid. We used the YESS 2.0 platform, combining protease and substrate expression in the yeast endoplasmic reticulum with fluorescence-activated cell sorting and next-generation sequencing, to carry out the high-resolution substrate specificity profiling of SARS-CoV-2 Mpro as well as the related SARS-CoV Mpro from SARS 2003. Even at such a high level of resolution, the substrate specificity profiles of both enzymes are essentially identical. The population of cleaved substrates isolated in our sorts is so deep, the relative catalytic efficiencies of the different cleavage sites on the SARS-CoV-2 polyproteins pp1a and pp2b are qualitatively predicted. These results not only demonstrated the precise and reproducible nature of the YESS 2.0/NGS approach to protease substrate specificity profiling but also should be useful in the design of next generation SARS-CoV-2 Mpro inhibitors, and by analogy, SARS-CoV Mpro inhibitors as well.

Craspase Orthologs Cleave a Nonconserved Site in Target Protein Csx30.

The Craspase CRISPR-Cas effector consists of the RNA-guided ribonuclease gRAMP and the protease TPR-CHAT, coupling target RNA recognition to protease activation. The natural substrate of Craspase is Csx30, a protein cleaved in two fragments that subsequently activates downstream antiviral pathways. Here, we determined the protease substrate specificity of Craspase from Candidatus "Jettenia caeni" (Jc-Craspase). We find that Jc-Craspase cleaves Jc-Csx30 in a target RNA-dependent fashion in A|S, which is different from the sites found in two other studied Craspases (L|D and M|K for Candidatus "Scalindua brodae" and Desulfonema ishimotonii, respectively). The fact that Craspase cleaves a nonconserved site across orthologs indicates the evolution of specific protein interactions between Craspase and its respective Csx30 target protein. The Craspase family thus represents a panel of proteases with different substrate specificities, which we exploited for the development of a readout for multiplexed RNA detection.

Molecular basis of TMPRSS2 recognition by Paeniclostridium sordellii hemorrhagic toxin

Hemorrhagic toxin (TcsH) is a major virulence factor produced by Paeniclostridium sordellii, which is a non-negligible threat to women undergoing childbirth or abortions. Recently, Transmembrane Serine Protease 2 (TMPRSS2) was identified as a host receptor of TcsH. Here, we show the cryo-EM structures of the TcsH-TMPRSS2 complex and uncover that TcsH binds to the serine protease domain (SPD) of TMPRSS2 through the CROP unit-VI. This receptor binding mode is unique among LCTs. Five top surface loops of TMPRSS2SPD, which also determine the protease substrate specificity, constitute the structural determinants recognized by TcsH. The binding of TcsH inhibits the proteolytic activity of TMPRSS2, whereas its implication in disease manifestations remains unclear. We further show that mutations selectively disrupting TMPRSS2-binding reduce TcsH toxicity in the intestinal epithelium of the female mice. These findings together shed light on the distinct molecular basis of TcsH-TMPRSS2 interactions, which expands our knowledge of host recognition mechanisms employed by LCTs and provides novel targets for developing therapeutics against P. sordellii infections.

A bioinformatics method for identification of human proteases active against viral envelope glycoproteins: a case study on the SARS-CoV-2 spike protein

Many viruses, including SARS-CoV-2, the coronavirus responsible for the COVID-19 pandemic, enter host cells through a process cell-viral membrane fusion that is activated by proteolytic enzymes. Typically, these enzymes are host cell proteases. Identifying the proteases that activate the virus is not a simple task but is important for the development of new antiviral drugs. In this study, we developed a bioinformatics method for identifying proteases that can cleave viral envelope glycoproteins. The proposed approach involves the use of predictive models for the substrate specificity of human proteases and the application of structural analysis method for predicting the vulnerability of protein regions to proteolysis based on their 3D structures. Specificity models were constructed for 169 human proteases using information on their known substrates. A previously developed method for structural analysis of potential proteolysis sites was applied in parallel with specificity models. Validation of the proposed approach was performed on the SARS-CoV-2 spike protein, the proteolysis sites of which had been well studied.

A Bioinformatics Method for Identification of Human Proteases Active against Viral Envelope Glycoproteins: A Case Study on the SARS-CoV-2 Spike Protein

Many viruses, including SARS-CoV-2, the coronavirus responsible for the COVID-19 pandemic, enter host cells through a process of cell-viral membrane fusion that is activated by proteolytic enzymes. Typically, these enzymes are host cell proteases. Identifying the proteases that activate the virus is not a simple task but is important for the development of new antiviral drugs. In this study, we developed a bioinformatics method for identifying proteases that can cleave viral envelope glycoproteins. The proposed approach involves the use of predictive models for the substrate specificity of human proteases and the application of a structural analysis method for predicting the vulnerability of protein regions to proteolysis based on their 3D structures. Specificity models were constructed for 169 human proteases using information on their known substrates. A previously developed method for structural analysis of potential proteolysis sites was applied in parallel with specificity models. Validation of the proposed approach was performed on the SARS-CoV-2 spike protein, whose proteolysis sites have been well studied.

Engineering New Protease Inhibitors Using α2-Macroglobulin.

Protease inhibitors of the alpha-macroglobulin family (αM) have a unique mechanism that allows them to trap proteases that is dependent not on the protease's class, but rather on its cleavage specificity. Proteases trigger a conformational change in the αM protein by cleaving within a "bait region," resulting in the sequestering of the protease inside the αM molecule. This nonspecific inhibitory mechanism appears to have arisen early in the αM family, and the broad protease-trapping capacity that it allows may play a role in pathogen defense.Human α2-macroglobulin (A2M) is a tetrameric αM whose bait region is permissive to cleavage by most proteases, making it a broad-spectrum protease inhibitor. Recent work has demonstrated that the inhibitory capacity of A2M derives directly from its bait region sequence: modifying the bait region sequence to introduce or remove protease cleavage sites will modify A2M's inhibition of the relevant proteases accordingly. Thus, changing the amino acid sequence of the bait region presents an effective avenue for protein engineering of new protease inhibitors if the substrate specificity of the target protease is known. The design of new A2M-based protease inhibitors with tailored inhibitory capacities has potential applications in basic research and the clinic. In this chapter, we describe the general approach and considerations for the bait region engineering of A2M.

ProsperousPlus: a one-stop and comprehensive platform for accurate protease-specific substrate cleavage prediction and machine-learning model construction.

Proteases contribute to a broad spectrum of cellular functions. Given a relatively limited amount of experimental data, developing accurate sequence-based predictors of substrate cleavage sites facilitates a better understanding of protease functions and substrate specificity. While many protease-specific predictors of substrate cleavage sites were developed, these efforts are outpaced by the growth of the protease substrate cleavage data. In particular, since data for 100+ protease types are available and this number continues to grow, it becomes impractical to publish predictors for new protease types, and instead it might be better to provide a computational platform that helps users to quickly and efficiently build predictors that address their specific needs. To this end, we conceptualized, developed, tested and released a versatile bioinformatics platform, ProsperousPlus, that empowers users, even those with no programming or little bioinformatics background, to build fast and accurate predictors of substrate cleavage sites. ProsperousPlus facilitates the use of the rapidly accumulating substrate cleavage data to train, empirically assess and deploy predictive models for user-selected substrate types. Benchmarking tests on test datasets show that our platform produces predictors that on average exceed the predictive performance of current state-of-the-art approaches. ProsperousPlus is available as a webserver and a stand-alone software package at http://prosperousplus.unimelb-biotools.cloud.edu.au/.

Substrate specificity of human chymotrypsin-like protease (CTRL) characterized by phage display-selected small-protein inhibitors

Chymotrypsin-like protease (CTRL) is one of the four chymotrypsin isoforms expressed in the human exocrine pancreas. Human genetic and experimental evidence indicate that chymotrypsins B1, B2, and C (CTRB1, CTRB2 and CTRC) are important not only for protein digestion but also for protecting the pancreas against pancreatitis by degrading potentially harmful trypsinogen. CTRL has not been reported to play a similar role, possibly due to its low abundance and/or different substrate specificity. To address this problem, we investigated the specificity of the substrate-binding groove of CTRL by evolving the substrate-like canonical loop of the Schistocerca gregaria proteinase inhibitor 2 (SGPI-2), a small-protein reversible chymotrypsin inhibitor to bind CTRL. We found that phage-associated SGPI-2 variants with strong affinity to CTRL were similar to those evolved previously against CTRB1, CTRB2 or bovine chymotrypsin A (bCTRA), indicating comparable substrate specificity. When tested as recombinant proteins, SGPI-2 variants inhibited CTRL with similar or slightly weaker affinity than bCTRA, confirming that CTRL is a typical chymotrypsin. Interestingly, an SGPI-2 variant selected with a Thr29His mutation in its reactive loop was found to inhibit CTRL strongly, but it was digested rapidly by bCTRA. Finally, CTRL was shown to degrade human anionic trypsinogen, however, at a much slower rate than CTRB2, suggesting that CTRL may not have a significant role in the pancreatic defense mechanisms against inappropriate trypsinogen activation and pancreatitis.

Multi-objective optimization with majority voting ensemble of classifiers for prediction of HIV-1 protease cleavage site

HIV-1 protease cleavage site prediction of an amino acid sequence of Human Immune Deficiency Virus (HIV-1) type 1 has been the subject of intense research for decades to increase the AUC value of the prediction without placing much attention to the accuracy metric by many researchers. Knowledge of the substrate specificity of HIV-1 protease has significant application in HIV-1 protease inhibitors development and in studying novel drug targets. Motivated by this, a multi-objective optimization (MOO)-based majority voting ensemble framework combining the outputs from multiple classifiers has been proposed in the current paper to increase both the prediction accuracy and AUC values simultaneously. The optimal set of classifiers that are considered for voting purposes at the time of combining the outputs is determined automatically using the search capability of MOO. Comparatively better results have been attained using various benchmark data sets with average accuracy and AUC (area under the ROC curve) values of 0.92 and 0.96, respectively.

Structure, Substrate Specificity and Role of Lon Protease in Bacterial Pathogenesis and Survival

Proteases are the group of enzymes that carry out proteolysis in all forms of life and play an essential role in cell survival. By acting on specific functional proteins, proteases affect the transcriptional and post-translational pathways in a cell. Lon, FtsH, HslVU and the Clp family are among the ATP-dependent proteases responsible for intracellular proteolysis in bacteria. In bacteria, Lon protease acts as a global regulator, governs an array of important functions such as DNA replication and repair, virulence factors, stress response and biofilm formation, among others. Moreover, Lon is involved in the regulation of bacterial metabolism and toxin-antitoxin systems. Hence, understanding the contribution and mechanisms of Lon as a global regulator in bacterial pathogenesis is crucial. In this review, we discuss the structure and substrate specificity of the bacterial Lon protease, as well as its ability to regulate bacterial pathogenesis.

Evaluating the Properties of Ginger Protease-Degraded Collagen Hydrolysate and Identifying the Cleavage Site of Ginger Protease by Using an Integrated Strategy and LC-MS Technology.

(1) Methods: An integrated strategy, including in vitro study (degree of hydrolysis (DH) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity) and in vivo study (absorption after oral administration in rats), was developed to evaluate the properties of the fish skin gelatin hydrolysates prepared using different proteases (pepsin, alkaline protease, bromelain, and ginger protease). Meanwhile, in order to identify the hydrolysis site of ginger protease, the peptides in the ginger protease-degraded collagen hydrolysate (GDCH) were comprehensively characterized by liquid chromatography/tandem mass spectrometry (LC-MS) method. (2) Results: The GDCH exhibited the highest DH (20.37%) and DPPH radical scavenging activity (77.73%), and in vivo experiments showed that the GDCH was more efficiently absorbed by the gastrointestinal tract. Further oral administration experiments revealed that GDCH was not entirely degraded to free amino acids and can be partially absorbed as dipeptides and tripeptides in intact forms, including Pro-Hyp, Gly-Pro-Hyp, and X-Hyp-Gly tripeptides. LC-MS results determined the unique substrate specificity of ginger protease recognizing Pro and Hyp at the P2 position based on the amino acids at the P2 position from the three types of tripeptides (Gly-Pro-Y, X-Hyp-Gly, and Z-Pro-Gly) and 136 identified peptides (>4 amino acids). Interestingly, it suggested that ginger protease can also recognize Ala in the P2 position. (3) Conclusions: This study comprehensively evaluated the properties of GDCH by combining in vitro and in vivo strategies, and is the first to identify the cleavage site of ginger protease by LC-MS technique. It provides support for the follow-up study on the commercial applications of ginger protease and bioactivities of the hydrolysate produced by ginger protease.

Structural basis for replicase polyprotein cleavage and substrate specificity of main protease from SARS-CoV-2

The main protease (Mpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a key enzyme, which extensively digests CoV replicase polyproteins essential for viral replication and transcription, making it an attractive target for antiviral drug development. However, the molecular mechanism of how Mpro of SARS-CoV-2 digests replicase polyproteins, releasing the nonstructural proteins (nsps), and its substrate specificity remain largely unknown. Here, we determine the high-resolution structures of SARS-CoV-2 Mpro in its resting state, precleavage state, and postcleavage state, constituting a full cycle of substrate cleavage. The structures show the delicate conformational changes that occur during polyprotein processing. Further, we solve the structures of the SARS-CoV-2 Mpro mutant (H41A) in complex with six native cleavage substrates from replicase polyproteins, and demonstrate that SARS-CoV-2 Mpro can recognize sequences as long as 10 residues but only have special selectivity for four subsites. These structural data provide a basis to develop potent new inhibitors against SARS-CoV-2.

Mechanism-based traps enable protease and hydrolase substrate discovery

Hydrolase enzymes, including proteases, are encoded by 2–3% of the genes in the human genome and 14% of these enzymes are active drug targets1. However, the activities and substrate specificities of many proteases—especially those embedded in membranes—and other hydrolases remain unknown. Here we report a strategy for creating mechanism-based, light-activated protease and hydrolase substrate traps in complex mixtures and live mammalian cells. The traps capture substrates of hydrolases, which normally use a serine or cysteine nucleophile. Replacing the catalytic nucleophile with genetically encoded 2,3-diaminopropionic acid allows the first step reaction to form an acyl-enzyme intermediate in which a substrate fragment is covalently linked to the enzyme through a stable amide bond2; this enables stringent purification and identification of substrates. We identify new substrates for proteases, including an intramembrane mammalian rhomboid protease RHBDL4 (refs. 3,4). We demonstrate that RHBDL4 can shed luminal fragments of endoplasmic reticulum-resident type I transmembrane proteins to the extracellular space, as well as promoting non-canonical secretion of endogenous soluble endoplasmic reticulum-resident chaperones. We also discover that the putative serine hydrolase retinoblastoma binding protein 9 (ref. 5) is an aminopeptidase with a preference for removing aromatic amino acids in human cells. Our results exemplify a powerful paradigm for identifying the substrates and activities of hydrolase enzymes.

Catalytic function and substrate specificity of alpha and beta coronavirus papain-like proteases: targeting human NL63, SARS-CoV and SARS-CoV-2 (Covid-19) with designed inhibitors

Coronaviruses (CoVs) are single-stranded, positive-sense RNA viruses in the Coronaviridae family. Within thisfamily are four genera: Alpha-, Beta-, Gamma-, and Delta Coronavirus, with Alpha- and Beta- containing coronaviruses that infect humans and animals. NL63 is an Alpha-CoV causing 15-30% of severe upper and lower respiratory tract infections each year. Severe Acute Respiratory Syndrome (SARS) and SARS-CoV-2 are Beta-CoVs that have affected many people worldwide during their outbreaks in 2002-2003 (SARS-CoV) and now in 2019-2020 (SARS-CoV-2).

Recent advances in fungal serine protease inhibitors

Four types of antifungal drugs are available that include inhibitors of ergosterol synthesis, of fungal RNA biosynthesis, and of cell wall biosynthesis as well as physiochemical regulators of fungal membrane sterols. Increasing resistance to antifungal drugs can severely limit treatment options of fungal nail infections, vaginal candidiasis, ringworm, blastomycosis, histoplasmosis, and Candida infections of the mouth, throat, and esophagus, among other infections. Development of strategies focused on new fungicides can effectively help tackle troublesome fungal diseases. The virulence and optimal growth of fungi depend on various extracellular secreted factors, among which proteases, such as serine proteases, are of particular interest. A specific extracellular proteolytic system enables fungi to survive and penetrate the tissues. Given the role of fungal proteases in infection, any molecule capable of selectively and specifically inhibiting their activity can lead to the development of potential drugs. Owing to their specific mode of action, fungal protease inhibitors can avoid fungal resistance observed with currently available treatments. Although fungal secreted proteases have been extensively studied as potential virulence factors, our understanding of the substrate specificity of such proteases remains poor. In this review, we summarize the recent advances in the design and development of specific serine protease inhibitors and provide a brief history of the compounds that inhibit fungal serine protease activity.

N-Terminomics Strategies for Protease Substrates Profiling.

Proteases play a central role in various biochemical pathways catalyzing and regulating key biological events. Proteases catalyze an irreversible post-translational modification called proteolysis by hydrolyzing peptide bonds in proteins. Given the destructive potential of proteolysis, protease activity is tightly regulated. Dysregulation of protease activity has been reported in numerous disease conditions, including cancers, neurodegenerative diseases, inflammatory conditions, cardiovascular diseases, and viral infections. The proteolytic profile of a cell, tissue, or organ is governed by protease activation, activity, and substrate specificity. Thus, identifying protease substrates and proteolytic events under physiological conditions can provide crucial information about how the change in protease regulation can alter the cellular proteolytic landscape. In recent years, mass spectrometry-based techniques called N-terminomics have become instrumental in identifying protease substrates from complex biological mixtures. N-terminomics employs the labeling and enrichment of native and neo-N-termini peptides, generated upon proteolysis followed by mass spectrometry analysis allowing protease substrate profiling directly from biological samples. In this review, we provide a brief overview of N-terminomics techniques, focusing on their strengths, weaknesses, limitations, and providing specific examples where they were successfully employed to identify protease substrates in vivo and under physiological conditions. In addition, we explore the current trends in the protease field and the potential for future developments.

Protease Substrate-Independent Universal Assay for Monitoring Digestion of Native Unmodified Proteins.

Proteases are a group of enzymes with a catalytic function to hydrolyze peptide bonds of proteins. Proteases regulate the activity, signaling mechanism, fate, and localization of many proteins, and their dysregulation is associated with various pathological conditions. Proteases have been identified as biomarkers and potential therapeutic targets for multiple diseases, such as acquired immunodeficiency syndrome, cardiovascular diseases, osteoporosis, type 2 diabetes, and cancer, where they are essential to disease progression. Thus, protease inhibitors and inhibitor-like molecules are interesting drug candidates. To study proteases and their substrates and inhibitors, simple, rapid, and sensitive protease activity assays are needed. Existing fluorescence-based assays enable protease monitoring in a high-throughput compatible microtiter plate format, but the methods often rely on either molecular labeling or synthetic protease targets that only mimic the hydrolysis site of the true target proteins. Here, we present a homogenous, label-free, and time-resolved luminescence utilizing the protein-probe method to assay proteases with native and denatured substrates at nanomolar sensitivity. The developed protein-probe method is not restricted to any single protein or protein target class, enabling digestion and substrate fragmentation studies with the natural unmodified substrate proteins. The versatility of the assay for studying protease targets was shown by monitoring the digestion of a substrate panel with different proteases. These results indicate that the protein-probe method not only monitors the protease activity and inhibition, but also studies the substrate specificity of individual proteases.

Predicting HIV-1 Protease Cleavage Sites With Positive-Unlabeled Learning.

Understanding the substrate specificity of HIV-1 protease plays an essential role in the prevention of HIV infection. A variety of computational models have thus been developed to predict substrate sites that are cleaved by HIV-1 protease, but most of them normally follow a supervised learning scheme to build classifiers by considering experimentally verified cleavable sites as positive samples and unknown sites as negative samples. However, certain noisy can be contained in the negative set, as false negative samples are possibly existed. Hence, the performance of the classifiers is not as accurate as they could be due to the biased prediction results. In this work, unknown substrate sites are regarded as unlabeled samples instead of negative ones. We propose a novel positive-unlabeled learning algorithm, namely PU-HIV, for an effective prediction of HIV-1 protease cleavage sites. Features used by PU-HIV are encoded from different perspectives of substrate sequences, including amino acid identities, coevolutionary patterns and chemical properties. By adjusting the weights of errors generated by positive and unlabeled samples, a biased support vector machine classifier can be built to complete the prediction task. In comparison with state-of-the-art prediction models, benchmarking experiments using cross-validation and independent tests demonstrated the superior performance of PU-HIV in terms of AUC, PR-AUC, and F-measure. Thus, with PU-HIV, it is possible to identify previously unknown, but physiologically existed substrate sites that are able to be cleaved by HIV-1 protease, thus providing valuable insights into designing novel HIV-1 protease inhibitors for HIV treatment.